Timeline for Why do we make convolutional neural networks longer and thinner between each layer?
Current License: CC BY-SA 4.0
3 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Aug 6, 2019 at 22:34 | comment | added | user2160809 | Each channel has it's own filters so different features of the data are learned. Channels is an easy way to structure the network so that multiple representaions are learned from the same input | |
| Aug 6, 2019 at 21:03 | comment | added | Anon | But why does having more channels allow for learning of global features? I'm not asking why we have multiple layers; I'm asking why we have multiple output channels that we then feed into fully connected layers at the end, as opposed to having a single output channel that we could then feed into some other layers. | |
| Aug 6, 2019 at 15:51 | history | answered | user2160809 | CC BY-SA 4.0 |